System and method for tagging signals of interest in time variant data

ABSTRACT

Disclosed herein are systems, computer-implemented methods, and computer-readable storage media for tagging a known signal of interest. Initially, the system classifies the data from an input signal using a short-term classifier, wherein there are at least two classifications available, a first classification of the data as having no identified outputs and a second classification of the data as at least one potential signal of interest, wherein the short-term classifier also bypasses data that is known to be of no interest. After the short-term classifier classifies the inputs, it collapses the input data that is classified as having no identified outputs. This allows the short-term classifier to create time-variant data. Finally, the system will tag a known signal of interest in the time-variant data that was classified as having at least one potential signal of interest. Therefore, a system for tagging a known signal of interest is described.

PRIORITY INFORMATION

The present application is a continuation of U.S. patent applicationSer. No. 12/582,067, filed Oct. 20, 2009, the contents of which isincorporated herein in its entirety.

BACKGROUND

1. Technical Field

The present disclosure relates to tagging signals and more specificallyto tagging voice signals of interest in time variant data.

2. Introduction

Currently, there are a number of different models that are used toidentify a signal of interest from a set of data. Typically, an analysistool analyzes the output of a signal classifier over a period of timeuntil the tool has enough information to identify a signal of interest.There are several well known forms of analysis that presently performthis function. Some examples are moving averages, least squares,convolution, and the Savitzky-Golay smoothing filter. However, thesemethods provide smoothing of the signal with respect to a measuredchange in time. Using time difference as a basis for smoothing has someconsequences, including the fact that when the signal is interrupted,the interruption can cause disruption in the system resulting in reducedsmoothing values. These systems have further problems including theinability to properly identify signals of interest when there aremultiple signals available and only one is of interest. Also, theseapproaches generally require pre-segmented data and multiple passes overthe data to generate an accurate identification.

SUMMARY

Additional features and advantages of the disclosure will be set forthin the description which follows, and in part will be obvious from thedescription, or can be learned by practice of the herein disclosedprinciples. The features and advantages of the disclosure can berealized and obtained by means of the instruments and combinationsparticularly pointed out in the appended claims. These and otherfeatures of the disclosure will become more fully apparent from thefollowing description and appended claims, or can be learned by thepractice of the principles set forth herein.

Disclosed are systems, computer-implemented methods, andcomputer-readable storage media for tagging signals of interest intime-variant data. Regarding the computer-implemented method, initially,a computer based system like that shown in FIG. 1, converts a timedomain input signal into the frequency domain so that features can beextracted from the input signal. A classifier then identifies aclassification for the input a signal based on the features extractedtherefrom. The classifier can classify the input signal into multipleclassifications including a category where the input has no identifiedoutputs or a category where the input signal has a potential signal ofinterest. Further, the classifier can bypass data that it knows is of nointerest to the system. The classifier can also create time-variant databy collapsing portions of the input data that are classified as havingno identified output. Then the classifier outputs that time-variant datato a signal tagger, which tags a signal known to be of interest to thesystem.

Further, another embodiment presently disclosed involves a system thatcan perform the method, such as is shown in FIG. 1 or can be representedby other appropriately designed hardware configurations. Included inthis system embodiment would be a processor that is in communicationwith a classifier, where the classifier can control the processor,thereby allowing the classifier to accept an input and classify it intoan appropriate category. Here, once again, the categories can be noidentifier outputs or an output containing a potential signal ofinterest, while the processor is also able to ignore signals known to beof no interest to the system. The classifier can then instruct theprocessor to collapse portions of the input signal that have noidentified output, creating time-variant data. Then a signal taggercontrols the processor to tag a signal known to be of interest to thesystem.

A final embodiment is a computer program that is stored on a computerreadable medium that has instructions for controlling the computer. Theinstructions control the system and tell the system to use a classifierto classify data from an input signal into an appropriate category.Specifically, the classifier classifies the signal as either having apotential signal of interest or having no recognized output. Further theinstructions would instruct the classifier to ignore signals known to beof no interest to the system. Next the instructions control theclassifier to form time-variant data by collapsing those portions of theinput that have no recognized output. Finally, the instructions controla signal tagger to tag any known signals of interest from thetime-variant data.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the disclosure can be obtained, a moreparticular description of the principles briefly described above will berendered by reference to specific embodiments thereof which areillustrated in the appended drawings. Understanding that these drawingsdepict only exemplary embodiments of the disclosure and are nottherefore to be considered to be limiting of its scope, the principlesherein are described and explained with additional specificity anddetail through the use of the accompanying drawings in which:

FIG. 1 illustrates an example system embodiment;

FIG. 2 illustrates a general embodiment of a computing device thatconforms to the present disclosure;

FIG. 3 illustrates a further general embodiment of a computing devicethat conforms to the present disclosure; and

FIG. 4 illustrates an example method embodiment.

DETAILED DESCRIPTION

Various embodiments of the disclosure are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationsmay be used without parting from the spirit and scope of the disclosure.

With reference to FIG. 1, an exemplary system 100 includes ageneral-purpose computing device 100, including a processing unit (CPUor processor) 120 and a system bus 110 that couples various systemcomponents including the system memory 130 such as read only memory(ROM) 140 and random access memory (RAM) 150 to the processor 120. Theseand other modules can be configured to control the processor 120 toperform various actions. Other system memory 130 may be available foruse as well. It can be appreciated that the disclosure may operate on acomputing device 100 with more than one processor 120 or on a group orcluster of computing devices networked together to provide greaterprocessing capability. The processor 120 can include any general purposeprocessor and a hardware module or software module, such as module 1162, module 2 164, and module 3 166 stored in storage device 160,configured to control the processor 120 as well as a special-purposeprocessor where software instructions are incorporated into the actualprocessor design. The processor 120 may essentially be a completelyself-contained computing system, containing multiple cores orprocessors, a bus, memory controller, cache, etc. A multi-core processormay be symmetric or asymmetric.

The system bus 110 may be any of several types of bus structuresincluding a memory bus or memory controller, a peripheral bus, and alocal bus using any of a variety of bus architectures. A basicinput/output (BIOS) stored in ROM 140 or the like, may provide the basicroutine that helps to transfer information between elements within thecomputing device 100, such as during start-up. The computing device 100further includes storage devices 160 such as a hard disk drive, amagnetic disk drive, an optical disk drive, tape drive or the like. Thestorage device 160 can include software modules 162, 164, 166 forcontrolling the processor 120. Other hardware or software modules arecontemplated. The storage device 160 is connected to the system bus 110by a drive interface. The drives and the associated computer readablestorage media provide nonvolatile storage of computer readableinstructions, data structures, program modules and other data for thecomputing device 100. In one aspect, a hardware module that performs aparticular function includes the software component stored in a tangibleand/or intangible computer-readable medium in connection with thenecessary hardware components, such as the processor 120, bus 110,display 170, and so forth, to carry out the function. Signals per se area form of transmission media through which the software componentsstored in a computer-readable storage medium may be transmitted. Thebasic components are known to those of skill in the art and appropriatevariations are contemplated depending on the type of device, such aswhether the device 100 is a small, handheld computing device, a desktopcomputer, or a computer server.

Although the exemplary embodiment described herein employs the hard disk160, it should be appreciated by those skilled in the art that othertypes of computer readable media which can store data that areaccessible by a computer, such as magnetic cassettes, flash memorycards, digital versatile disks, cartridges, random access memories(RAMs) 150, read only memory (ROM) 140, a cable or wireless signalcontaining a bit stream and the like, may also be used in the exemplaryoperating environment. Computer-readable storage media expressly excludemedia such as energy, carrier signals, electromagnetic waves, andsignals per se.

To enable user interaction with the computing device 100, an inputdevice 190 represents any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input, speech and so forth. The inputdevice 190 may be used by the presenter to indicate the beginning of aspeech search query. An output device 170 can also be one or more of anumber of output mechanisms known to those of skill in the art. In someinstances, multimodal systems enable a user to provide multiple types ofinput to communicate with the computing device 100. The communicationsinterface 180 generally governs and manages the user input and systemoutput. There is no restriction on operating on any particular hardwarearrangement and therefore the basic features here may easily besubstituted for improved hardware or firmware arrangements as they aredeveloped.

For clarity of explanation, the illustrative system embodiment ispresented as including individual functional blocks including functionalblocks labeled as a “processor” or processor 120. The functions theseblocks represent may be provided through the use of either shared ordedicated hardware, including, but not limited to, hardware capable ofexecuting software and hardware, such as a processor 120, that ispurpose-built to operate as an equivalent to software executing on ageneral purpose processor. For example the functions of one or moreprocessors presented in FIG. 1 may be provided by a single sharedprocessor or multiple processors. (Use of the term “processor” shouldnot be construed to refer exclusively to hardware capable of executingsoftware.) Illustrative embodiments may include microprocessor, digitalsignal processor (DSP), field programmable gate arrays (FPGAs), and/orapplication specific integrated circuits (ASICs) hardware, read-onlymemory (ROM) 140 for storing software performing the operationsdiscussed below, and random access memory (RAM) 150 for storing results.Very large scale integration (VLSI) hardware embodiments, as well ascustom VLSI circuitry in combination with a general purpose DSP circuit,may also be provided.

The logical operations of the various embodiments are implemented as:(1) a sequence of computer implemented steps, operations, or proceduresrunning on a programmable circuit within a general use computer, (2) asequence of computer implemented steps, operations, or proceduresrunning on a specific-use programmable circuit; and/or (3)interconnected machine modules or program engines within theprogrammable circuits. The system 100 shown in FIG. 1 can practice allor part of the recited methods, can be a part of the recited systems,and/or can operate according to instructions in the recited tangiblecomputer-readable storage media. Generally speaking, such logicaloperations can be implemented as modules configured to control theprocessor 120 to perform particular functions according to theprogramming of the module. For example, FIG. 1 illustrates three modulesMod1 162, Mod2 164 and Mod3 166 which are modules configured to controlthe processor 120. These modules may be stored on the storage device 160and loaded into RAM 150 or memory 130 at runtime or may be stored aswould be known in the art in other computer-readable memory locations.

Having disclosed some basic system components, the disclosure now turnsto the exemplary method embodiment shown in FIG. 2. For the sake ofclarity, the method is discussed in terms of an exemplary system such asis shown in FIG. 1 configured to practice the method.

FIG. 2 illustrates a first embodiment classifier that classifies inputdata using the short-term classifier 210 to narrow the input data to apotential signal of interest, and then uses the signal tagger 220 toidentify and tag the potential signal of interest. As an initialexample, the input data can be audio signals and the short-termclassifier 210 and the signal tagger 220 can be used to identify thespeaker of the audio signal. When the signal of interest is a voice, thesystem converts the time domain audio sample to a sequence of frequencydomain vectors. This transformation can take place in a number ofdifferent ways using mathematical transformation operations like Laplaceand Fourier transforms. From these frequency domain vectors, the systemextracts features to allow for pattern classification. Such features caninclude Mel-frequency cepstral coefficients (MFCC), first and secondderivatives of MFCCs, prosody including aspects of formants and pitch,and induced higher order features such as Principal Component Analysis(PCA), however, a person of skill in the art will be familiar withmultiple other features that can be extracted.

Frequently, when voices are the signal of interest, there will be adatabase of known voices representing the person or persons that are ofinterest. This database can be a separate module within the system;however, it is presently represented as being part of the short-termclassifier 210. Further, the database can be used to train theshort-term classifier 210 to recognize when a person of interest's voiceis within the original audio sample. This allows the audio sample to bean input into the trained short-term classifier 210, where theshort-term classifier outputs values that indicate there is a signal ofinterest within the audio sample. These values are stepped over time andmay fluctuate depending on the changing nature of the input data. Then,a signal tagger 220 analyzes these outputs to find and identifycandidate signals of interest in real-time without the need to analyzethe data using multiple passes.

In one aspect, the signal tagger 220 analyzes the short-termclassifier's output and computes smoothed results. Initially, the signaltagger 220 will determine the number of matches (hits) identified withinthe short-term classifier outputs, where the outputs are based on theshort-term classifier input vectors. When a trained speaker's voice isidentified as present, the output values will be “1”, otherwise thevalue will be “0”. Depending on the classifier, the system can determinethese output values by a decision method such as a threshold.

The signal tagger 220 then sums the hits for each output over a frame ofdata where the frame size is determined by the number of hits for themost likely candidate signal of interest. In one embodiment, this framesize is defined as when a total of 2 seconds of hits (an adjustableparameter) have been collected for the most likely candidate from theshort-term classifier. The most likely candidate is evaluated every 0.5seconds. However, a person of skill in art, with the use of the presentdisclosure, will be able to determine the ideal frame size, based on thesignals being analyzed. In one aspect, a system practicing the methodcan learn the optimal period for a speaker specific model. The systemcan base the learning on a signal profile, a history, and/or otherrelated parameters. The short-term classifier 210 can also label itsoutputs. For purposes of example, and in no way meant to be limiting,the present embodiment will use three primary categories when analyzinginputs for speaker recognition. The three categories are: one output,multiple outputs, and no outputs. The one output category is used whenthe short-term classifier 210 outputs a single signal of interest; itcan be beneficial to have the short-term classifier 210 focus on oneoutput for each input vector. The multiple outputs category is usedwhere there are multiple signals present, such as two different voiceconversations in the same environment. Finally, the no outputs categoryis used when no outputs for the classifier are true, for instance whenthe classifier encounters a signal that it has never seen before.

For the “one output” category, there are some instances where more thanone output could occur. The first of a non-limiting group includes whenthere is more than one speaker of interest talking at the same time. Asecond example is when there are different noises present that cause theshort-term classifier 210 to interpret a new signal that is notidentified to appear as though it satisfies the trained conditions.Therefore, one of the advantages of using the one output category is tosum only the classifier outputs where one signal is identified, therebyallowing for the rejection of weak conditions where the score would bebiased because of the two signals being seen as active. So, the signaltagger 220 waits until there is only one signal of interest present andthereby excludes some of the cases that can cause the signal tagger tomiss-identify a signal.

To further explain the multiple outputs category, it is possible for theshort-term classifier 210 to identify more than one signal of interestin the input data. For instance if there is a conversation between fourpersons of interest, then the short-term classifier would output foursignals of interest to the signal tagger 220. This would allow thesignal tagger 220 to switch between the four persons in the conversationas each talked in real time.

The disclosure next further explains the “no output” category and how itis used. In one example, the short-term classifier 210 encounters asignal that it has never seen before. During these periods where thesignal cannot be classified, the short-term classifier 210 can ignore orcollapse these periods of time to create a time-variant structure ordata streams. This time-variant data is then analyzed by the signaltagger 220 and smoothed. A further aspect of the no output category isthat when a period of no output is observed for a significant amount oftime, the signal tagger 220 can then tag that signal as a potentialsignal of interest and add it to the database of known signals, eventhough the source is still unknown. This potential signal of interest isthen available for use in analyzing future data for tagging.

Collapsing can take place using multiple techniques, of which anon-limiting group of examples will be described. One method ofcollapsing is where the data is actually removed from the stream by theshort-term classifier 210, resulting in an output where the frame onlyaccepts data that is allowed through the short term classifier andthereby collecting 2 seconds of data, per the above example. Anothertechnique involves ignoring the data during the periods of no output,thereby functionally collapsing that period without actually removingthe data. This will cause the smoothing window of 2 seconds, per theabove example, to grow as needed since the ignored portions do not countor contribute to the data used in the smoothing process.

The classifier can be trained to recognize the background ambient noisefloor and unwanted non-speech sounds such as door slams, paper rattle,air conditioning equipment, overhead airplanes, dial tones, etc. Thesystem can group such non-speech sounds into a non-speech category whichis collapsible if no speaker of interest classifier output signals arepresent. Some sounds are not clearly “non-speech”, such as a cough or aspeaker clearing his throat. The system can decide whether to includesuch borderline sounds with the speaker of interest signal because thesounds are characteristic of the subject speaker.

One of the consequences of having signals of no interest is that whenonly signals of no interest are present and the short-term classifier210 ignores those signals, that period of time will be considered tohave no output and collapsed as if the short-term classifier has nooutput. A similar situation will occur when both signals of no interestand signals that fit into the no output category are present, becausethe short-term classifier 210 will bypass the signals of no interest andcollapse the no output signals, thereby effectively collapsing thesignals of no interest as well. When the signal of no interest ispresent along with a signal of interest, then the signal of no interestis bypassed and ignored to reduce the noise in the signal and providefor a more effective tagging. Some non-limiting examples of signals ofno interest are system background noise, paper rattles, thumps, or evenknown speakers of no interest. Those of skill in the art will be able tofurther distinguish sounds that are of interest and those of nointerest. A helpful addition to the database can be known signals of nointerest so that the classifier can easily exclude those known soundsthat are not of use in tagging signals of interest.

Next, in accumulating the running hit totals, the classifier can beginto compute scores and separation values for these totals. In oneexemplary embodiment, the running hit totals are given by the followingequation:

${{Score}(x)} = {100*{\sin\left( {\frac{\sum\limits_{i = 1}^{n}{hits}_{xi}}{{totalhits} - {bghits}}*90} \right)}}$

where:

-   x=index representing the particular signal of interest; individual    scores are computed for each classifier signal of interest-   n=the number of evaluated input vectors for a smoothing period—where    the length of the smoothing period is dependent on the amount of    data collected for the best candidate-   hits=the selected classifier output result (1 or 0) for true/false    for a classifier input vector-   i=the input vector index for the selected time period-   totalhits=the accumulated sum of all classifier outputs over the    selected time period-   bghits=hits for anything identified as “signal of no interest” (such    as system background noise, paper rattles, thumps, and even other    known speakers not of interest).-   Note: the sin( )function is optional; it provides a human-factors    adjustment to skew the scores into a higher range of values.    Relative position of the scores remain unchanged without the    function. The constant “90” scales the hits ratio to 0-90 degrees    for the sin( )function. Therefore, the above equation can be    simplified to:

${{Score}(x)} = \frac{\sum\limits_{i = 1}^{n}{hits}_{xi}}{{totalhits} - {bghits}}$

These scores are then used to determine which outputs from theclassifier have the highest likelihood of being the signal of interest.To make this determination, the system looks at the signal that has thehighest score over the selected period. Next, the signal with thehighest score is monitored over multiple selected frames, allowing thetotal score to increase in a short-term smoothing function. Anotheraspect of this embodiment is that there are cases where the signal withthe highest score can change between the selected periods. When thisoccurs, the amount of time needed to accumulate data adapts to a periodneeded for the most likely signal of interest as does the time until thesmoothing function is observed. Therefore, the system utilizes as muchtime as is necessary to accumulate sufficient amounts of data toproperly tag a signal of interest. This means that in the presentembodiment, the signal tagger tags results that are based on theclassifier data, independent of the period. The best candidate for thesignal of interest and the associated amount of time for the signaltagger accumulator is recalculated at each selected period of time, anda smoothed signal tagger output is computed for each classifiercategory.

Another calculation that may help in identifying the signal of interestis the separation between the most likely candidate for being the signalof interest and the second most likely candidate for signal of interest.

${separation} = {\frac{\left( {{n\; 1} - {n\; 2}} \right)}{\left( {{n\; 1} + {n\; 2}} \right)}*100}$

Where,

-   n1=score for the most likely candidate-   n2=score for the 2nd most likely candidate

It turns out that the separation and the score are highly correlated, asseparation is inherently considered for all classifier categories by thescore computation. The separation value can serve the further purpose ofhelping an analyst who is later reviewing the data to understand wheresimilar signals are showing up and where the classifier is having adifficult time.

If enough non-speech accumulates between speech, then the systemimplements a shut-off condition, effectively resetting the smoothingfunctions and disabling the results until enough speech is againencountered. This effectively clamps the outputs of the signal tagger tozero during longer periods of non-speech. The reset condition is basedon an adjustable level, m, when

totahits≧m* bghits

While the previous embodiment dealt primarily with voice identification,there are many other areas where the method of signal tagging can beused. The uses for the method are very broad—the technology applies toany signal classification problem where dynamic tagging of theoccurrence of a signal of interest is needed, non-limiting classifierexamples include Gaussian mixture models, Support Vector Machines,neural networks, and learning machine architectures. One such examplecan be signals intelligence applications, including signal detection,signal identification, targeting, signal channel identification, etc.Areas of value to the Intelligence Community include SIGINT, COMINT,ELINT, and MASINT.

For commercial applications, the signal tagger 220 could be employed totrigger adaptive systems that tune into identified signals in a broadspectrum of capability. For example, automated speech recognition (voiceto text) would be enhanced very quickly for a recognized speaker byadapting the speech model to the characteristics of that speaker's voice(or family of voice).

In addition to adaptive systems, the signal tagger 220 could be used inauthentication systems to verify the user of a device or for access toareas or equipment. The signal tagger 220 could be used to validateremote employees who are a party to sensitive conference calls.

The signal tagger 220 can also be used in learning machines. Forexample, if a set of known signals are normally encountered, and a newone shows up, the signal tagger 220 can be used to identify the newsignal as one of a class of signals—but unknown. The new signal can besubmitted to a classifier for training and be added to the knowledgebasefor future encounters. This approach could be used in network fraud andabuse analysis by tagging voice calls and associating the tags in aknowledgebase with other calls for contact chaining and social networkanalysis.

The short-term classifier 210 system employed with the signal tagger 220is not limited to voice applications. It can be applied to any sensorystimuli that can be represented by a digital signal. The signal tagger220 is also not limited to a particular classifier, and could beemployed to enhance many existing applications.

Further, this embodiment can be gated to be active or inactive based onthe number of samples that are being collected. The measurement of thenumber of samples can take place using at least two different methods.One such method is to determine the number of samples heuristicallybased on current or previous results. A second is measuring the samplerate automatically, and adjusting the period by looking at the signaltagger output so that intermittent output switching is minimized oroptimized.

FIG. 3 represents another embodiment within the teachings of the presentdisclosure. In this embodiment, the signal tagger and short-termclassifier are combined into a single module 310 to both classify theinput data, and tag a signal of interest all in the same step. Thisembodiment would therefore not output the signal of interest to thesignal tagger but rather would take the classified signal and tag itthereby creating only one output.

FIG. 4 represents the method that is performed or executed by a systemor computing device like that shown in FIG. 1 or any other appropriatehardware configuration. As shown in FIG. 4, the system classifies, via aprocessor, the input signal using the short term classifier, like thatshown as 210 in FIG. 2. The classifications include data classified ashaving no identified outputs and data classified as having a potentialsignal of interest. Also, the system can bypass data that it knows is ofno interest. Then the system can create time-variant data by collapsingthe portions of the input signal that are classified as having noidentified outputs. Finally, the system will tag a signal of interestusing a signal tagger like the one shown as 220 in FIG. 2.

Another embodiment deals with a tangible computer readable storagemedium storing a computer program having instructions for controlling acomputing device to tag known signals of interest, the instructionscausing the computing device to perform the steps just described andshown in FIG. 4. The computer system would be configured using thehardware as shown in FIG. 1 or any other appropriate hardwareconfiguration.

Embodiments within the scope of the present disclosure may also includetangible computer-readable storage media for carrying or havingcomputer-executable instructions or data structures stored thereon. Suchcomputer-readable storage media can be any available media that can beaccessed by a general purpose or special purpose computer, including thefunctional design of any special purpose processor as discussed above.By way of example, and not limitation, such computer-readable media caninclude RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magneticdisk storage or other magnetic storage devices, or any other mediumwhich can be used to carry or store desired program code means in theform of computer-executable instructions, data structures, or processorchip design. When information is transferred or provided over a networkor another communications connection (either hardwired, wireless, orcombination thereof) to a computer, the computer properly views theconnection as a computer-readable medium. Thus, any such connection isproperly termed a computer-readable medium. Combinations of the aboveshould also be included within the scope of the computer-readable media.

Computer-executable instructions include, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Computer-executable instructions also includeprogram modules that are executed by computers in stand-alone or networkenvironments. Generally, program modules include routines, programs,components, data structures, objects, and the functions inherent in thedesign of special-purpose processors, etc. that perform particular tasksor implement particular abstract data types. Computer-executableinstructions, associated data structures, and program modules representexamples of the program code means for executing steps of the methodsdisclosed herein. The particular sequence of such executableinstructions or associated data structures represents examples ofcorresponding acts for implementing the functions described in suchsteps.

Those of skill in the art will appreciate that other embodiments of thedisclosure may be practiced in network computing environments with manytypes of computer system configurations, including personal computers,hand-held devices, multi-processor systems, microprocessor-based orprogrammable consumer electronics, network PCs, minicomputers, mainframecomputers, and the like. Embodiments may also be practiced indistributed computing environments where tasks are performed by localand remote processing devices that are linked (either by hardwiredlinks, wireless links, or by a combination thereof) through acommunications network. In a distributed computing environment, programmodules may be located in both local and remote memory storage devices.

The various embodiments described above are provided by way ofillustration only and should not be construed to limit the scope of thedisclosure. For example, the principles herein can be applied to anysignal from radio frequencies to thermal imaging. Those skilled in theart will readily recognize various modifications and changes that may bemade to the principles described herein without following the exampleembodiments and applications illustrated and described herein, andwithout departing from the spirit and scope of the disclosure.

1. A method comprising: classifying, via a processor, a first data frominput signal data as having no signal of interest, to yield a firstclassification; classifying a second data from the input signal data ashaving a potential signal of interest, to yield a second classification;collapsing a portion of the input signal data, based on the firstclassification, to yield a third data; and tagging a segment of thethird data, based on the second classification, as having a known signalof interest.
 2. The method of claim 1, wherein the collapsing furthercomprises at least one of ignoring a portion of the input signal dataand removing a portion of the input signal data.
 3. The method of claim1, wherein having no signal of interest comprises at least one of havingno identified outputs and having a known signal of no interest.
 4. Themethod of claim 3, wherein the known signal of no interest comprises oneof a door slam, a paper rattle, air conditioning, an airplane engine, adial tone, and a speaker of no interest.
 5. The method of claim 1,wherein the third data is a time-variant data.
 6. The method of claim 1,wherein the potential signal of interest is a signal with no output fora predetermined amount of time.
 7. The method of claim 1, wherein thethird data is smoothed prior to the tagging but after the collapsing. 8.The method of claim 1, wherein the tagging further comprises determininga number of matches identified within the third data based on knownsignals of interest.
 9. A system comprising: a processor; and acomputer-readable storage medium storing instructions which, whenexecuted by the processor, cause the processor to perform a methodcomprising: classifying a first data from input signal data as having nosignal of interest, to yield a first classification; classifying asecond data from the input signal data as having a potential signal ofinterest, to yield a second classification; collapsing a portion of theinput signal data, based on the first classification, to yield a thirddata; and tagging a segment of the third data, based on the secondclassification, as having a known signal of interest.
 10. The system ofclaim 9, wherein the collapsing further comprises at least one ofignoring a portion of the input signal data and removing a portion ofthe input signal data.
 11. The system of claim 9, wherein having nosignal of interest comprises at least one of having no identifiedoutputs and having a known signal of no interest.
 12. The system ofclaim 9, wherein the third data is a time-variant data.
 13. The systemof claim 9, wherein the third data is smoothed prior to the tagging butafter the collapsing.
 14. The system of claim 9, wherein the taggingfurther comprises determining a number of matches identified within thethird data based on known signals of interest.
 15. A computer-readablestorage medium storing instructions which, when executed by a processor,cause the processor to perform a method comprising: classifying a firstdata from input signal data as having no signal of interest, to yield afirst classification; classifying a second data from the input signaldata as having a potential signal of interest, to yield a secondclassification; collapsing a portion of the input signal data, based onthe first classification, to yield a third data; and tagging a segmentof the third data, based on the second classification, as having a knownsignal of interest.
 16. The computer-readable storage medium of claim15, wherein the collapsing further comprises at least one of ignoring aportion of the input signal data and removing a portion of the inputsignal data.
 17. The computer-readable storage medium of claim 15,wherein having no signal of interest comprises at least one of having noidentified outputs and having a known signal of no interest.
 18. Thecomputer-readable storage medium of claim 17, wherein the known signalof no interest comprises one of a door slam, a paper rattle, airconditioning, an airplane engine, a dial tone, and a speaker of nointerest.
 19. The computer-readable storage medium of claim 15, whereinthe third data is a time-variant data.
 20. The computer-readable storagemedium of claim 15, wherein the potential signal of interest is a signalwith no output for a predetermined amount of time.